Many industrial, scientific, and medical applications require the capture and classification of analog signals from analog signal sources such as sensor devices. Signals carry various types of information corresponding to physical phenomena. For example, in response to incident light, an electronic photosensor outputs a voltage proportional to the intensity of the incident light. The output voltage is an analog signal that may be detected and classified to identify certain characteristics of the light source, such as light intensity. Other applications include classification of signals output from pressure transducers, thermal sensors, and biomedical devices. Events in the physical sources of signals are manifested in the form of signal features, such as time-amplitude patterns or signatures, generated by the sensor device in response to such events. For example, a sudden rise in pressure may be manifested in the form of a pulse (a sharply rising and falling signal) in the output of a pressure transducer. Depending on the application, the method and apparatus for signal classification varies. For example, in some applications, such as control of machinery, real-time signal processing is required. In other applications, the signal generated from the sensor device may be digitized and stored for later processing. Generally, processing and classification of signals may be performed in a single apparatus or distributed across several devices and systems, depending on application requirements. In applications where power and space are not constrained, the signal from a sensor device may be transmitted to another device or system, such as a digital computing device, for processing. Alternatively, the sensor device and the processing device may be housed in a single apparatus. Various system configurations for signal acquisition and processing are determined based on many factors including cost of equipment, flexibility of operations, power and space constraints, and specific application constraints and requirements. For example, a laboratory setting for measurement of pressure in a pressure vessel imposes different requirements than pressure measurements in a running engine on board a moving vehicle.
Signals output from sensor devices are generally analog signals. Classifying signals may be done digitally or using analog techniques. Digitization of signals provides certain advantages such as flexibility, storage for future processing, and record keeping. On the other hand, signal digitization requires additional hardware, which increases cost and time of processing. Additional hardware also requires additional power and space, so that systems used for processing of digitized signals consume more power and are bulkier. In some applications, the additional size and power consumption cause serious problems to the point that the application may not be possible or practical.
In biomedical applications such as design of biomedical devices like pacemakers and neural implants, classification of patterns and waveforms present in physiological signals is often essential. Waveforms in physiological signals are classified for several reasons, including (1) detecting abnormalities so a corrective action or diagnosis can be performed; and (2) correlating with other signals or behavior for physiological system identification. One method of classifying waveforms and temporal signals includes verifying the waveforms' compliance to a set of amplitude windows. For example, a neurophysiological signal's waveform can be classified by specifying a threshold, voltage ranges in the amplitude window, and time delay from a threshold-crossing to the amplitude window. Classification methods based on the amplitude window techniques are well known in the art and may be easily implemented using software or digital hardware. However, as indicated above, system configurations requiring digitization of signals and processing of the digitized signals require additional power and hardware, making certain biomedical applications, such as neural implants, unfeasible.